Overview

Dataset statistics

Number of variables28
Number of observations744
Missing cells11939
Missing cells (%)57.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory162.9 KiB
Average record size in memory224.2 B

Variable types

Categorical9
Numeric9
Unsupported10

Alerts

Longitude (x) has constant value "-79.4" Constant
Latitude (y) has constant value "43.67" Constant
Station Name has constant value "TORONTO" Constant
Climate ID has constant value "6158350" Constant
Year has constant value "1969" Constant
Month has constant value "5" Constant
Date/Time (LST) has a high cardinality: 744 distinct values High cardinality
Temp (°C) is highly correlated with Dew Point Temp (°C) and 1 other fieldsHigh correlation
Dew Point Temp (°C) is highly correlated with Temp (°C) and 3 other fieldsHigh correlation
Rel Hum (%) is highly correlated with Dew Point Temp (°C)High correlation
Stn Press (kPa) is highly correlated with Dew Point Temp (°C)High correlation
Hmdx is highly correlated with Temp (°C) and 1 other fieldsHigh correlation
Longitude (x) is highly correlated with Latitude (y)High correlation
Latitude (y) is highly correlated with Longitude (x)High correlation
Day is highly correlated with HmdxHigh correlation
Temp (°C) is highly correlated with Dew Point Temp (°C) and 1 other fieldsHigh correlation
Dew Point Temp (°C) is highly correlated with Temp (°C) and 2 other fieldsHigh correlation
Rel Hum (%) is highly correlated with Dew Point Temp (°C)High correlation
Hmdx is highly correlated with Day and 2 other fieldsHigh correlation
Temp (°C) is highly correlated with HmdxHigh correlation
Dew Point Temp (°C) is highly correlated with HmdxHigh correlation
Hmdx is highly correlated with Temp (°C) and 1 other fieldsHigh correlation
Month is highly correlated with Station Name and 6 other fieldsHigh correlation
Station Name is highly correlated with Month and 6 other fieldsHigh correlation
Year is highly correlated with Month and 6 other fieldsHigh correlation
Weather is highly correlated with Month and 5 other fieldsHigh correlation
Time (LST) is highly correlated with Month and 5 other fieldsHigh correlation
Climate ID is highly correlated with Month and 6 other fieldsHigh correlation
Longitude (x) is highly correlated with Month and 6 other fieldsHigh correlation
Latitude (y) is highly correlated with Month and 6 other fieldsHigh correlation
Day is highly correlated with Temp (°C) and 4 other fieldsHigh correlation
Temp (°C) is highly correlated with Day and 4 other fieldsHigh correlation
Dew Point Temp (°C) is highly correlated with Day and 4 other fieldsHigh correlation
Rel Hum (%) is highly correlated with Day and 4 other fieldsHigh correlation
Wind Dir (10s deg) is highly correlated with Wind Spd (km/h)High correlation
Wind Spd (km/h) is highly correlated with Wind Dir (10s deg) and 1 other fieldsHigh correlation
Visibility (km) is highly correlated with Hmdx and 1 other fieldsHigh correlation
Stn Press (kPa) is highly correlated with Day and 4 other fieldsHigh correlation
Hmdx is highly correlated with Temp (°C) and 3 other fieldsHigh correlation
Weather is highly correlated with Day and 3 other fieldsHigh correlation
Temp (°C) has 319 (42.9%) missing values Missing
Temp Flag has 744 (100.0%) missing values Missing
Dew Point Temp (°C) has 319 (42.9%) missing values Missing
Dew Point Temp Flag has 744 (100.0%) missing values Missing
Rel Hum (%) has 319 (42.9%) missing values Missing
Rel Hum Flag has 744 (100.0%) missing values Missing
Wind Dir (10s deg) has 627 (84.3%) missing values Missing
Wind Dir Flag has 744 (100.0%) missing values Missing
Wind Spd (km/h) has 620 (83.3%) missing values Missing
Wind Spd Flag has 744 (100.0%) missing values Missing
Visibility (km) has 644 (86.6%) missing values Missing
Visibility Flag has 744 (100.0%) missing values Missing
Stn Press (kPa) has 620 (83.3%) missing values Missing
Stn Press Flag has 744 (100.0%) missing values Missing
Hmdx has 712 (95.7%) missing values Missing
Hmdx Flag has 744 (100.0%) missing values Missing
Wind Chill has 744 (100.0%) missing values Missing
Wind Chill Flag has 744 (100.0%) missing values Missing
Weather has 319 (42.9%) missing values Missing
Date/Time (LST) is uniformly distributed Uniform
Time (LST) is uniformly distributed Uniform
Date/Time (LST) has unique values Unique
Temp Flag is an unsupported type, check if it needs cleaning or further analysis Unsupported
Dew Point Temp Flag is an unsupported type, check if it needs cleaning or further analysis Unsupported
Rel Hum Flag is an unsupported type, check if it needs cleaning or further analysis Unsupported
Wind Dir Flag is an unsupported type, check if it needs cleaning or further analysis Unsupported
Wind Spd Flag is an unsupported type, check if it needs cleaning or further analysis Unsupported
Visibility Flag is an unsupported type, check if it needs cleaning or further analysis Unsupported
Stn Press Flag is an unsupported type, check if it needs cleaning or further analysis Unsupported
Hmdx Flag is an unsupported type, check if it needs cleaning or further analysis Unsupported
Wind Chill is an unsupported type, check if it needs cleaning or further analysis Unsupported
Wind Chill Flag is an unsupported type, check if it needs cleaning or further analysis Unsupported
Dew Point Temp (°C) has 19 (2.6%) zeros Zeros

Reproduction

Analysis started2022-01-06 14:18:16.810646
Analysis finished2022-01-06 14:18:37.211104
Duration20.4 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Longitude (x)
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
-79.4
744 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-79.4
2nd row-79.4
3rd row-79.4
4th row-79.4
5th row-79.4

Common Values

ValueCountFrequency (%)
-79.4744
100.0%

Length

2022-01-06T09:18:37.352726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-06T09:18:37.471917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
79.4744
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Latitude (y)
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
43.67
744 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43.67
2nd row43.67
3rd row43.67
4th row43.67
5th row43.67

Common Values

ValueCountFrequency (%)
43.67744
100.0%

Length

2022-01-06T09:18:37.566664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-06T09:18:37.714296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
43.67744
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Station Name
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
TORONTO
744 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTORONTO
2nd rowTORONTO
3rd rowTORONTO
4th rowTORONTO
5th rowTORONTO

Common Values

ValueCountFrequency (%)
TORONTO744
100.0%

Length

2022-01-06T09:18:37.819018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-06T09:18:37.917752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
toronto744
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Climate ID
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
6158350
744 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6158350
2nd row6158350
3rd row6158350
4th row6158350
5th row6158350

Common Values

ValueCountFrequency (%)
6158350744
100.0%

Length

2022-01-06T09:18:38.041421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-06T09:18:38.136169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
6158350744
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Date/Time (LST)
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct744
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
1969-05-05 00:00
 
1
1969-05-24 17:00
 
1
1969-05-06 01:00
 
1
1969-05-30 14:00
 
1
1969-05-26 12:00
 
1
Other values (739)
739 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique744 ?
Unique (%)100.0%

Sample

1st row1969-05-01 00:00
2nd row1969-05-01 01:00
3rd row1969-05-01 02:00
4th row1969-05-01 03:00
5th row1969-05-01 04:00

Common Values

ValueCountFrequency (%)
1969-05-05 00:001
 
0.1%
1969-05-24 17:001
 
0.1%
1969-05-06 01:001
 
0.1%
1969-05-30 14:001
 
0.1%
1969-05-26 12:001
 
0.1%
1969-05-22 18:001
 
0.1%
1969-05-20 21:001
 
0.1%
1969-05-09 02:001
 
0.1%
1969-05-26 00:001
 
0.1%
1969-05-30 05:001
 
0.1%
Other values (734)734
98.7%

Length

2022-01-06T09:18:38.216951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11:0031
 
2.1%
20:0031
 
2.1%
08:0031
 
2.1%
05:0031
 
2.1%
21:0031
 
2.1%
23:0031
 
2.1%
18:0031
 
2.1%
15:0031
 
2.1%
07:0031
 
2.1%
13:0031
 
2.1%
Other values (45)1178
79.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Year
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
1969
744 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1969
2nd row1969
3rd row1969
4th row1969
5th row1969

Common Values

ValueCountFrequency (%)
1969744
100.0%

Length

2022-01-06T09:18:38.352591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-06T09:18:38.479287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1969744
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Month
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
5
744 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5744
100.0%

Length

2022-01-06T09:18:38.569765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-06T09:18:38.675483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
5744
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-06T09:18:38.812118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.950288912
Coefficient of variation (CV)0.559393057
Kurtosis-1.202512518
Mean16
Median Absolute Deviation (MAD)8
Skewness0
Sum11904
Variance80.1076716
MonotonicityIncreasing
2022-01-06T09:18:38.964750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3124
 
3.2%
1524
 
3.2%
224
 
3.2%
324
 
3.2%
424
 
3.2%
524
 
3.2%
624
 
3.2%
724
 
3.2%
824
 
3.2%
924
 
3.2%
Other values (21)504
67.7%
ValueCountFrequency (%)
124
3.2%
224
3.2%
324
3.2%
424
3.2%
524
3.2%
624
3.2%
724
3.2%
824
3.2%
924
3.2%
1024
3.2%
ValueCountFrequency (%)
3124
3.2%
3024
3.2%
2924
3.2%
2824
3.2%
2724
3.2%
2624
3.2%
2524
3.2%
2424
3.2%
2324
3.2%
2224
3.2%

Time (LST)
Categorical

HIGH CORRELATION
UNIFORM

Distinct24
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
09:00
 
31
11:00
 
31
17:00
 
31
07:00
 
31
04:00
 
31
Other values (19)
589 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00
2nd row01:00
3rd row02:00
4th row03:00
5th row04:00

Common Values

ValueCountFrequency (%)
09:0031
 
4.2%
11:0031
 
4.2%
17:0031
 
4.2%
07:0031
 
4.2%
04:0031
 
4.2%
05:0031
 
4.2%
03:0031
 
4.2%
20:0031
 
4.2%
10:0031
 
4.2%
14:0031
 
4.2%
Other values (14)434
58.3%

Length

2022-01-06T09:18:39.116336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:0031
 
4.2%
13:0031
 
4.2%
11:0031
 
4.2%
17:0031
 
4.2%
07:0031
 
4.2%
04:0031
 
4.2%
05:0031
 
4.2%
03:0031
 
4.2%
20:0031
 
4.2%
10:0031
 
4.2%
Other values (14)434
58.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Temp (°C)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct50
Distinct (%)11.8%
Missing319
Missing (%)42.9%
Infinite0
Infinite (%)0.0%
Mean14.40211765
Minimum4.4
Maximum33.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-06T09:18:39.250942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4.4
5-th percentile7.8
Q110.6
median13.9
Q317.2
95-th percentile25.48
Maximum33.9
Range29.5
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation5.392134404
Coefficient of variation (CV)0.3743987194
Kurtosis1.564930612
Mean14.40211765
Median Absolute Deviation (MAD)3.3
Skewness1.095584341
Sum6120.9
Variance29.07511343
MonotonicityNot monotonic
2022-01-06T09:18:39.446240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.628
 
3.8%
15.624
 
3.2%
1523
 
3.1%
11.121
 
2.8%
16.721
 
2.8%
12.820
 
2.7%
14.419
 
2.6%
1019
 
2.6%
9.418
 
2.4%
8.918
 
2.4%
Other values (40)214
28.8%
(Missing)319
42.9%
ValueCountFrequency (%)
4.41
 
0.1%
52
 
0.3%
5.62
 
0.3%
6.14
 
0.5%
6.76
 
0.8%
7.26
 
0.8%
7.811
1.5%
8.310
1.3%
8.918
2.4%
9.418
2.4%
ValueCountFrequency (%)
33.91
 
0.1%
33.31
 
0.1%
32.81
 
0.1%
32.21
 
0.1%
31.14
0.5%
30.62
0.3%
301
 
0.1%
29.42
0.3%
27.82
0.3%
27.22
0.3%

Temp Flag
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing744
Missing (%)100.0%
Memory size5.9 KiB

Dew Point Temp (°C)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct47
Distinct (%)11.1%
Missing319
Missing (%)42.9%
Infinite0
Infinite (%)0.0%
Mean5.296705882
Minimum-6.1
Maximum21.1
Zeros19
Zeros (%)2.6%
Negative73
Negative (%)9.8%
Memory size5.9 KiB
2022-01-06T09:18:39.610592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-6.1
5-th percentile-2.2
Q10.6
median4.4
Q38.9
95-th percentile15.6
Maximum21.1
Range27.2
Interquartile range (IQR)8.3

Descriptive statistics

Standard deviation5.735328984
Coefficient of variation (CV)1.082810545
Kurtosis-0.3604046504
Mean5.296705882
Median Absolute Deviation (MAD)3.8
Skewness0.5465927835
Sum2251.1
Variance32.89399856
MonotonicityNot monotonic
2022-01-06T09:18:39.783702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
4.423
 
3.1%
0.620
 
2.7%
2.219
 
2.6%
019
 
2.6%
1.719
 
2.6%
7.819
 
2.6%
-0.617
 
2.3%
5.615
 
2.0%
-1.715
 
2.0%
6.714
 
1.9%
Other values (37)245
32.9%
(Missing)319
42.9%
ValueCountFrequency (%)
-6.11
 
0.1%
-52
 
0.3%
-4.43
 
0.4%
-3.95
 
0.7%
-3.36
 
0.8%
-2.83
 
0.4%
-2.29
1.2%
-1.715
2.0%
-1.112
1.6%
-0.617
2.3%
ValueCountFrequency (%)
21.11
 
0.1%
201
 
0.1%
19.43
0.4%
18.94
0.5%
18.32
 
0.3%
17.85
0.7%
17.22
 
0.3%
16.72
 
0.3%
15.65
0.7%
155
0.7%

Dew Point Temp Flag
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing744
Missing (%)100.0%
Memory size5.9 KiB

Rel Hum (%)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct70
Distinct (%)16.5%
Missing319
Missing (%)42.9%
Infinite0
Infinite (%)0.0%
Mean56.74823529
Minimum27
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-06T09:18:40.122106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile32
Q143
median54
Q370
95-th percentile89
Maximum97
Range70
Interquartile range (IQR)27

Descriptive statistics

Standard deviation17.27783925
Coefficient of variation (CV)0.3044647849
Kurtosis-0.6547302375
Mean56.74823529
Median Absolute Deviation (MAD)13
Skewness0.4520649936
Sum24118
Variance298.5237292
MonotonicityNot monotonic
2022-01-06T09:18:40.279381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5117
 
2.3%
4014
 
1.9%
6012
 
1.6%
4512
 
1.6%
4311
 
1.5%
3411
 
1.5%
4211
 
1.5%
6411
 
1.5%
5810
 
1.3%
4610
 
1.3%
Other values (60)306
41.1%
(Missing)319
42.9%
ValueCountFrequency (%)
271
 
0.1%
283
 
0.4%
292
 
0.3%
303
 
0.4%
317
0.9%
328
1.1%
332
 
0.3%
3411
1.5%
356
0.8%
366
0.8%
ValueCountFrequency (%)
972
 
0.3%
963
 
0.4%
952
 
0.3%
949
1.2%
931
 
0.1%
913
 
0.4%
901
 
0.1%
893
 
0.4%
882
 
0.3%
873
 
0.4%

Rel Hum Flag
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing744
Missing (%)100.0%
Memory size5.9 KiB

Wind Dir (10s deg)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)12.8%
Missing627
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean19.16239316
Minimum2
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-06T09:18:40.420437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q19
median20
Q327
95-th percentile36
Maximum36
Range34
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.251241543
Coefficient of variation (CV)0.4827811153
Kurtosis-0.9961225957
Mean19.16239316
Median Absolute Deviation (MAD)7
Skewness0.215139401
Sum2242
Variance85.58547009
MonotonicityNot monotonic
2022-01-06T09:18:40.564506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
928
 
3.8%
2013
 
1.7%
1812
 
1.6%
2711
 
1.5%
2510
 
1.3%
369
 
1.2%
147
 
0.9%
236
 
0.8%
344
 
0.5%
294
 
0.5%
Other values (5)13
 
1.7%
(Missing)627
84.3%
ValueCountFrequency (%)
21
 
0.1%
54
 
0.5%
73
 
0.4%
928
3.8%
147
 
0.9%
163
 
0.4%
1812
1.6%
2013
1.7%
236
 
0.8%
2510
 
1.3%
ValueCountFrequency (%)
369
1.2%
344
 
0.5%
322
 
0.3%
294
 
0.5%
2711
1.5%
2510
1.3%
236
0.8%
2013
1.7%
1812
1.6%
163
 
0.4%

Wind Dir Flag
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing744
Missing (%)100.0%
Memory size5.9 KiB

Wind Spd (km/h)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct21
Distinct (%)16.9%
Missing620
Missing (%)83.3%
Infinite0
Infinite (%)0.0%
Mean15.82258065
Minimum0
Maximum37
Zeros7
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-06T09:18:40.730065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q110
median16
Q323
95-th percentile29
Maximum37
Range37
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.637268827
Coefficient of variation (CV)0.5458824335
Kurtosis-0.7040619572
Mean15.82258065
Median Absolute Deviation (MAD)7
Skewness0.0001890789775
Sum1962
Variance74.6024128
MonotonicityNot monotonic
2022-01-06T09:18:40.850776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1616
 
2.2%
2414
 
1.9%
199
 
1.2%
108
 
1.1%
118
 
1.1%
238
 
1.1%
07
 
0.9%
57
 
0.9%
66
 
0.8%
86
 
0.8%
Other values (11)35
 
4.7%
(Missing)620
83.3%
ValueCountFrequency (%)
07
0.9%
34
 
0.5%
57
0.9%
66
 
0.8%
86
 
0.8%
108
1.1%
118
1.1%
133
 
0.4%
143
 
0.4%
1616
2.2%
ValueCountFrequency (%)
371
 
0.1%
351
 
0.1%
323
 
0.4%
293
 
0.4%
273
 
0.4%
264
 
0.5%
2414
1.9%
238
1.1%
215
 
0.7%
199
1.2%

Wind Spd Flag
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing744
Missing (%)100.0%
Memory size5.9 KiB

Visibility (km)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)12.0%
Missing644
Missing (%)86.6%
Infinite0
Infinite (%)0.0%
Mean21.27
Minimum3.2
Maximum32.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-06T09:18:40.993832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile8
Q116.1
median24.1
Q324.1
95-th percentile32.2
Maximum32.2
Range29
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.791654118
Coefficient of variation (CV)0.3193067286
Kurtosis0.08249114028
Mean21.27
Median Absolute Deviation (MAD)4.8
Skewness-0.4496778544
Sum2127
Variance46.12656566
MonotonicityNot monotonic
2022-01-06T09:18:41.095556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
24.143
 
5.8%
16.119
 
2.6%
32.213
 
1.7%
19.39
 
1.2%
11.33
 
0.4%
12.93
 
0.4%
253
 
0.4%
4.82
 
0.3%
82
 
0.3%
3.21
 
0.1%
Other values (2)2
 
0.3%
(Missing)644
86.6%
ValueCountFrequency (%)
3.21
 
0.1%
4.82
 
0.3%
6.41
 
0.1%
82
 
0.3%
9.71
 
0.1%
11.33
 
0.4%
12.93
 
0.4%
16.119
2.6%
19.39
 
1.2%
24.143
5.8%
ValueCountFrequency (%)
32.213
 
1.7%
253
 
0.4%
24.143
5.8%
19.39
 
1.2%
16.119
2.6%
12.93
 
0.4%
11.33
 
0.4%
9.71
 
0.1%
82
 
0.3%
6.41
 
0.1%

Visibility Flag
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing744
Missing (%)100.0%
Memory size5.9 KiB

Stn Press (kPa)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct95
Distinct (%)76.6%
Missing620
Missing (%)83.3%
Infinite0
Infinite (%)0.0%
Mean100.1733871
Minimum97.47
Maximum101.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-06T09:18:41.244635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum97.47
5-th percentile98.619
Q199.83
median100.235
Q3100.7775
95-th percentile101.17
Maximum101.51
Range4.04
Interquartile range (IQR)0.9475

Descriptive statistics

Standard deviation0.8008967404
Coefficient of variation (CV)0.007995104924
Kurtosis0.9061301367
Mean100.1733871
Median Absolute Deviation (MAD)0.475
Skewness-0.9368218273
Sum12421.5
Variance0.6414355888
MonotonicityNot monotonic
2022-01-06T09:18:41.443102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.223
 
0.4%
100.713
 
0.4%
100.123
 
0.4%
100.612
 
0.3%
101.122
 
0.3%
100.332
 
0.3%
100.132
 
0.3%
100.352
 
0.3%
101.132
 
0.3%
100.412
 
0.3%
Other values (85)101
 
13.6%
(Missing)620
83.3%
ValueCountFrequency (%)
97.471
0.1%
97.851
0.1%
98.031
0.1%
98.371
0.1%
98.431
0.1%
98.51
0.1%
98.611
0.1%
98.671
0.1%
98.721
0.1%
98.741
0.1%
ValueCountFrequency (%)
101.511
0.1%
101.441
0.1%
101.391
0.1%
101.291
0.1%
101.182
0.3%
101.172
0.3%
101.152
0.3%
101.132
0.3%
101.122
0.3%
101.111
0.1%

Stn Press Flag
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing744
Missing (%)100.0%
Memory size5.9 KiB

Hmdx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)40.6%
Missing712
Missing (%)95.7%
Infinite0
Infinite (%)0.0%
Mean32.21875
Minimum25
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2022-01-06T09:18:41.578739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile25
Q126.75
median33
Q338
95-th percentile39
Maximum40
Range15
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation5.493306044
Coefficient of variation (CV)0.1705002846
Kurtosis-1.620343336
Mean32.21875
Median Absolute Deviation (MAD)5
Skewness-0.09490877605
Sum1031
Variance30.17641129
MonotonicityNot monotonic
2022-01-06T09:18:41.703441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
257
 
0.9%
385
 
0.7%
334
 
0.5%
373
 
0.4%
393
 
0.4%
282
 
0.3%
312
 
0.3%
261
 
0.1%
271
 
0.1%
401
 
0.1%
Other values (3)3
 
0.4%
(Missing)712
95.7%
ValueCountFrequency (%)
257
0.9%
261
 
0.1%
271
 
0.1%
282
 
0.3%
291
 
0.1%
301
 
0.1%
312
 
0.3%
334
0.5%
361
 
0.1%
373
0.4%
ValueCountFrequency (%)
401
 
0.1%
393
0.4%
385
0.7%
373
0.4%
361
 
0.1%
334
0.5%
312
 
0.3%
301
 
0.1%
291
 
0.1%
282
 
0.3%

Hmdx Flag
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing744
Missing (%)100.0%
Memory size5.9 KiB

Wind Chill
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing744
Missing (%)100.0%
Memory size5.9 KiB

Wind Chill Flag
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing744
Missing (%)100.0%
Memory size5.9 KiB

Weather
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12
Distinct (%)2.8%
Missing319
Missing (%)42.9%
Memory size5.9 KiB
Mostly Cloudy
129 
Mainly Clear
104 
Cloudy
80 
Clear
73 
Rain,Fog
 
12
Other values (7)
27 

Length

Max length16
Median length12
Mean length9.595294118
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.9%

Sample

1st rowClear
2nd rowClear
3rd rowClear
4th rowClear
5th rowClear

Common Values

ValueCountFrequency (%)
Mostly Cloudy129
17.3%
Mainly Clear104
 
14.0%
Cloudy80
 
10.8%
Clear73
 
9.8%
Rain,Fog12
 
1.6%
Rain Showers10
 
1.3%
Haze10
 
1.3%
Rain3
 
0.4%
Rain Showers,Fog1
 
0.1%
Fog1
 
0.1%
Other values (2)2
 
0.3%
(Missing)319
42.9%

Length

2022-01-06T09:18:41.877454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cloudy209
31.2%
clear177
26.4%
mostly129
19.3%
mainly104
15.5%
rain14
 
2.1%
rain,fog13
 
1.9%
haze10
 
1.5%
showers10
 
1.5%
heavy1
 
0.1%
drizzle1
 
0.1%
Other values (2)2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-01-06T09:18:32.598548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:20.672872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:22.340753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:23.782629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:25.182558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:26.842793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:28.203796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:29.657040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:31.110454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:32.752137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:20.926617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:22.496303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:23.932544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:25.375519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:26.985987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:28.370363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:29.792295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:31.266037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:32.901737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:21.095166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:22.662893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:24.082629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:25.570585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:27.125204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:28.535910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:29.924904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:31.437635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:33.055454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:21.370172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:22.806092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:24.225651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:25.732742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:27.279798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:28.682516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:30.060540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:31.617160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:33.201066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:21.554714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:22.958680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:24.407649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:26.030419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:27.451426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:28.841098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:30.199169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:31.788166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:33.329721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:21.711631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:23.106982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:24.552721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:26.188508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:27.611128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:28.995229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:30.487566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:31.943786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:33.482844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:21.858498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:23.290457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:24.720647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:26.373012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:27.755767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:29.146299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:30.663094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:32.118284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:33.654394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:22.000629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:23.443122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:24.869669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:26.533618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:27.907038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:29.310859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:30.797284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:32.266956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:33.797004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:22.182211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:23.625597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:25.029639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:26.696183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:28.073142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:29.493376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:30.952866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-06T09:18:32.446956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-01-06T09:18:42.077918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-06T09:18:42.515671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-06T09:18:43.259681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-06T09:18:43.960836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-06T09:18:44.453029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-06T09:18:34.107307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-06T09:18:35.910089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-06T09:18:36.469342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-06T09:18:36.877943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Longitude (x)Latitude (y)Station NameClimate IDDate/Time (LST)YearMonthDayTime (LST)Temp (°C)Temp FlagDew Point Temp (°C)Dew Point Temp FlagRel Hum (%)Rel Hum FlagWind Dir (10s deg)Wind Dir FlagWind Spd (km/h)Wind Spd FlagVisibility (km)Visibility FlagStn Press (kPa)Stn Press FlagHmdxHmdx FlagWind ChillWind Chill FlagWeather
0-79.443.67TORONTO61583501969-05-01 00:0019695100:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1-79.443.67TORONTO61583501969-05-01 01:0019695101:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2-79.443.67TORONTO61583501969-05-01 02:0019695102:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3-79.443.67TORONTO61583501969-05-01 03:0019695103:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4-79.443.67TORONTO61583501969-05-01 04:0019695104:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5-79.443.67TORONTO61583501969-05-01 05:0019695105:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6-79.443.67TORONTO61583501969-05-01 06:0019695106:006.1NaN-3.9NaN48.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNClear
7-79.443.67TORONTO61583501969-05-01 07:0019695107:007.2NaN-3.3NaN46.0NaNNaNNaN0.0NaN16.1NaN101.11NaNNaNNaNNaNNaNClear
8-79.443.67TORONTO61583501969-05-01 08:0019695108:0010.6NaN-2.2NaN41.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNClear
9-79.443.67TORONTO61583501969-05-01 09:0019695109:0012.2NaN-1.1NaN39.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNClear

Last rows

Longitude (x)Latitude (y)Station NameClimate IDDate/Time (LST)YearMonthDayTime (LST)Temp (°C)Temp FlagDew Point Temp (°C)Dew Point Temp FlagRel Hum (%)Rel Hum FlagWind Dir (10s deg)Wind Dir FlagWind Spd (km/h)Wind Spd FlagVisibility (km)Visibility FlagStn Press (kPa)Stn Press FlagHmdxHmdx FlagWind ChillWind Chill FlagWeather
734-79.443.67TORONTO61583501969-05-31 14:00196953114:0017.2NaN1.7NaN34.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNClear
735-79.443.67TORONTO61583501969-05-31 15:00196953115:0017.2NaN0.0NaN31.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNClear
736-79.443.67TORONTO61583501969-05-31 16:00196953116:0017.2NaN0.6NaN33.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNClear
737-79.443.67TORONTO61583501969-05-31 17:00196953117:0017.2NaN0.0NaN31.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMostly Cloudy
738-79.443.67TORONTO61583501969-05-31 18:00196953118:0017.2NaN-0.6NaN30.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMostly Cloudy
739-79.443.67TORONTO61583501969-05-31 19:00196953119:0016.1NaN0.0NaN34.0NaN9.0NaN27.0NaN24.1NaN99.83NaNNaNNaNNaNNaNMainly Clear
740-79.443.67TORONTO61583501969-05-31 20:00196953120:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
741-79.443.67TORONTO61583501969-05-31 21:00196953121:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
742-79.443.67TORONTO61583501969-05-31 22:00196953122:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
743-79.443.67TORONTO61583501969-05-31 23:00196953123:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN